Faik Baskaya

Bio: Faik Baskaya is an academic researcher from Boğaziçi University. The author has contributed to research in topics: Field-programmable analog array & Analogue electronics. The author has an hindex of 10, co-authored 28 publications receiving 336 citations. Previous affiliations of Faik Baskaya include Georgia Institute of Technology.

A Floating-Gate-Based Field-Programmable Analog Array

Abstract: A field-programmable analog array (FPAA) with 32 computational analog blocks (CABs) and occupying 3 × 3 mm2 in 0.35-μm CMOS is presented. Each CAB has a wide variety of subcircuits ranging in granularity from multipliers and programmable offset wide-linear-range Gm blocks to nMOS and pMOS transistors. The programmable interconnects and circuit elements in the CAB are implemented using floating-gate (FG) transistors, the total number of which exceeds fifty thousand. Using FG devices eliminates the need for SRAM to store configuration bits since the switch stores its own configuration. This system exhibits significant performance enhancements over its predecessor in terms of achievable dynamic range (> 9 b of FG voltage) and speed (≈ 20 gates/s) of accurate FG current programming and isolation between ON and OFF switches. An improved routing fabric has been designed that includes nearest neighbor connections to minimize the penalty on bandwidth due to routing parasitic. A maximum bandwidth of 57 MHz through the switch matrix and around 5 MHz for a first-order low-pass filter is achievable on this chip, the limitation being a “program” mode switch that will be rectified in the next chip. Programming performance improved drastically by implementing the entire algorithm on-chip with an SPI digital interface. Measured results of the individual subcircuits and two system examples including an AM receiver and a speech processor are presented.

Placement for large-scale floating-gate field-programable analog arrays

Abstract: Modern advances in reconfigurable analog technologies are allowing field-programmable analog arrays (FPAAs) to dramatically grow in size, flexibility, and usefulness. Our goal in this paper is to develop the first placement algorithm for large-scale floating-gate-based FPAAs with a focus on the minimization of the parasitic effects on interconnects under various device-related constraints. Our FPAA clustering algorithm first groups analog components into a set of clusters so that the total number of routing switches used is minimized and all IO paths are balanced in terms of routing switches used. Our FPAA placement algorithm then maps each cluster to a computational analog block (CAB) of the target FPAA while focusing on routing switch usage and balance again. Experimental results demonstrate the effectiveness of our approach

Rapid Prototyping of Large-scale Analog Circuits With Field Programmable Analog Array

Abstract: Modern advances in reconfigurable analog technologies are allowing field-programmable analog arrays (FPAAs) to dramatically grow in size, flexibility, and usefulness. This paper presents rapid prototyping results of a bandpass filter as a sample analog circuit using our floating-gate based large-scale FPAA. A major source of parasitics introduced during the circuit mapping process is interconnect switches used for routing. Our goal is to obtain models of the mapped circuits that can be simulated using SPICE in order to observe the impact of interconnect parasitics on the relevant analog metrics. Our results indicate that the mapped analog circuits obtain desired responses even with interconnect parasitics, clearly demonstrating the practicality of our FPAA.

A novel yield aware multi-objective analog circuit optimization tool

Abstract: This paper proposes a novel multi-objective yield aware analog sizing tool that utilizes scrambled Quasi Monte Carlo (QMC) approach for efficient yield estimation and Strength Pareto Evolutionary Algorithm-2 (SPEA2) as a search engine. Analog circuit sizing tools have been utilized for the last two decades to overcome challenging trade-offs in analog circuit design. However, due to the variation phenomenon, some solutions at the Pareto front (PF) move towards the suboptimal region. To overcome this issue, yield aware optimization tools, where yield is given as a new design objective, have been proposed in the last decade. Conventionally, Monte Carlo (MC) approach has been used for the yield estimation. However, large sized MC analysis is a highly inefficient and time consuming process because of the numerous simulations performed during the optimization process. Rather than conventional MC, using QMC, which utilizes Low Discrepancy Sequences (LDS), enhances the synthesis time since, it promises low estimation errors with fewer number of simulations. Thanks to the QMC based variability analysis and multi-objective search engine, a yield aware PF that allows the designer to access all robust solutions can be obtained within an acceptable synthesis time.

Adaptive sized Quasi-Monte Carlo based yield aware analog circuit optimization tool

Abstract: This paper proposes an efficient Quasi-Monte Carlo based yield aware analog circuit synthesis tool with an adaptive sampling mechanism. Monte Carlo (MC) analysis is commonly preferred to estimate process variation effects on the yield of manufactured ICs. However, conventional MC requires a large number of simulations for accurate estimation. This situation causes excessive synthesis times during yield aware optimization, where many iterative variability simulations are performed. To enhance the efficiency, Infeasible Solution Elimination approach is utilized, in which yield estimation is not performed for infeasible solutions. In addition to this approach, a more efficient MC method, called Quasi-Monte Carlo (QMC), is used to generate samples from the uncertain parameter space. Thanks to the homogeneous distribution of samples, the required number of simulations is substantially reduced. Furthermore, QMC allows iterative generation of samples; hence enabling the sample size to be increased without restarting the entire simulation from the beginning. Using this property of QMC, an adaptive mechanism is proposed to determine the minimum sample size required for accurate yield estimation. The yield term is introduced as a new design constraint to the optimizer together with the electrical constraints. Finally, the developed tool offers an additional part, where a simulation budget allocation algorithm promises a more accurate yield estimation for the valuable candidates.

Experimental Results and Modeling Techniques for Substrate Noise in Mixed-Signal Integrated Circuits

Abstract: An experimental technique is described for observing the effects of switching transients in digital MOS circuits that perturb analog circuits integrated on the same die by means of coupling through the substrate Various approaches to reducing substrate crosstalk (the use of physical separation of analog and digital circuits, guard rings, and a low-inductance substrate bias) are evaluated experimentally for a CMOS technology with a substrate comprising an epitaxial layer grown on a heavily doped bulk wafer Observations indicate that reducing the inductance in the substrate bias is the most effective Device simulations are used to show how crosstalk propagates via the heavily doped bulk and to predict the nature of substrate crosstalk in CMOS technologies integrated in uniform, lightly doped bulk substrates, showing that in such cases the substrate noise is highly dependent on layout geometry A method of including substrate effects in SPICE simulations for circuits fabricated on epitaxial, heavily doped substrates is developed >

Current Trends in Remote Laboratories

Abstract: Remote laboratories have been introduced during the last few decades into engineering education processes as well as integrated within e-learning frameworks offered to engineering and science students. Remote laboratories are also being used to support life-long learning and student's autonomous learning activities. In this paper, after a brief overview of state-of-the-art technologies in the development of remote laboratories and presentation of recent and interesting examples of remote laboratories in several areas related with industrial electronics education, some current trends and challenges are also identified and discussed.

Finding a roadmap to achieve large neuromorphic hardware systems.

Abstract: Neuromorphic systems are gaining increasing importance in an era where CMOS digital computing techniques are meeting hard physical limits. These silicon systems mimic extremely energy efficient neural computing structures, potentially both for solving engineering applications as well as understanding neural computation. Towards this end, the authors provide a glimpse at what the technology evolution roadmap looks like for these systems so that Neuromorphic engineers may gain the same benefit of anticipation and foresight that IC designers gained from Moore's law many years ago. Scaling of energy efficiency, performance, and size will be discussed as well as how the implementation and application space of Neuromorphic systems are expected to evolve over time.

A Floating-Gate-Based Field-Programmable Analog Array

Abstract: A field-programmable analog array (FPAA) with 32 computational analog blocks (CABs) and occupying 3 × 3 mm2 in 0.35-μm CMOS is presented. Each CAB has a wide variety of subcircuits ranging in granularity from multipliers and programmable offset wide-linear-range Gm blocks to nMOS and pMOS transistors. The programmable interconnects and circuit elements in the CAB are implemented using floating-gate (FG) transistors, the total number of which exceeds fifty thousand. Using FG devices eliminates the need for SRAM to store configuration bits since the switch stores its own configuration. This system exhibits significant performance enhancements over its predecessor in terms of achievable dynamic range (> 9 b of FG voltage) and speed (≈ 20 gates/s) of accurate FG current programming and isolation between ON and OFF switches. An improved routing fabric has been designed that includes nearest neighbor connections to minimize the penalty on bandwidth due to routing parasitic. A maximum bandwidth of 57 MHz through the switch matrix and around 5 MHz for a first-order low-pass filter is achievable on this chip, the limitation being a “program” mode switch that will be rectified in the next chip. Programming performance improved drastically by implementing the entire algorithm on-chip with an SPI digital interface. Measured results of the individual subcircuits and two system examples including an AM receiver and a speech processor are presented.

Large-scale field-programmable analog arrays for analog signal processing

Abstract: Field-programmable analog arrays (FPAAs) provide a method for rapidly prototyping analog systems. Currently available commercial and academic FPAAs are typically based on operational amplifiers (or other similar analog primitives) with only a few computational elements per chip. While their specific architectures vary, their small sizes and often restrictive interconnect designs leave current FPAAs limited in functionality and flexibility. For FPAAs to enter the realm of large-scale reconfigurable devices such as modern field-programmable gate arrays (FPGAs), new technologies must be explored to provide area-efficient accurately programmable analog circuitry that can be easily integrated into a larger digital/mixed-signal system. Recent advances in the area of floating-gate transistors have led to a core technology that exhibits many of these qualities, and current research promises a digitally controllable analog technology that can be directly mated to commercial FPGAs. By leveraging these advances, a new generation of FPAAs is introduced in this paper that will dramatically advance the current state of the art in terms of size, functionality, and flexibility. FPAAs have been fabricated using floating-gate transistors as the sole programmable element, and the results of characterization and system-level experiments on the most recent FPAA are shown.